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Early prognosis of respiratory virus shedding in humans.
Aminian, M; Ghosh, T; Peterson, A; Rasmussen, A L; Stiverson, S; Sharma, K; Kirby, M.
Afiliación
  • Aminian M; Department of Mathematics and Statistics, California State Polytechnic University, Pomona, CA, USA.
  • Ghosh T; Department of Computer Science, Colorado State University, Fort Collins, CO, 80524, USA.
  • Peterson A; Department of Mathematics, Colorado State University, Fort Collins, CO, 80524, USA.
  • Rasmussen AL; Vaccine and Infectious Disease Organization-International Vaccine Centre (VIDO-InterVac), University of Saskatchewan, Saskatoon, SK, Canada.
  • Stiverson S; Center for Global Health Science and Security, Georgetown University Medical Center, Washington, DC, USA.
  • Sharma K; Department of Mathematics, Colorado State University, Fort Collins, CO, 80524, USA.
  • Kirby M; Department of Computer Science, Colorado State University, Fort Collins, CO, 80524, USA.
Sci Rep ; 11(1): 17193, 2021 08 25.
Article en En | MEDLINE | ID: mdl-34433834
This paper addresses the development of predictive models for distinguishing pre-symptomatic infections from uninfected individuals. Our machine learning experiments are conducted on publicly available challenge studies that collected whole-blood transcriptomics data from individuals infected with HRV, RSV, H1N1, and H3N2. We address the problem of identifying discriminatory biomarkers between controls and eventual shedders in the first 32 h post-infection. Our exploratory analysis shows that the most discriminatory biomarkers exhibit a strong dependence on time over the course of the human response to infection. We visualize the feature sets to provide evidence of the rapid evolution of the gene expression profiles. To quantify this observation, we partition the data in the first 32 h into four equal time windows of 8 h each and identify all discriminatory biomarkers using sparsity-promoting classifiers and Iterated Feature Removal. We then perform a comparative machine learning classification analysis using linear support vector machines, artificial neural networks and Centroid-Encoder. We present a range of experiments on different groupings of the diseases to demonstrate the robustness of the resulting models.
Asunto(s)

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Simulación por Computador / Esparcimiento de Virus / Infecciones por Virus Sincitial Respiratorio / Infecciones por Picornaviridae / Gripe Humana / Transcriptoma Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: Sci Rep Año: 2021 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Simulación por Computador / Esparcimiento de Virus / Infecciones por Virus Sincitial Respiratorio / Infecciones por Picornaviridae / Gripe Humana / Transcriptoma Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: Sci Rep Año: 2021 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido